Muratoglu Abdullah, Bilgen Gonca Karaca, Angin Ilker, Kodal Suleyman
Department of Civil Engineering, Batman University, Faculty of Engineering and Architecture, Batman 72100, Türkiye.
General Directorate of Agricultural Research and Policies, Ankara 06800, Türkiye.
Water Res. 2023 Jun 30;238:120011. doi: 10.1016/j.watres.2023.120011. Epub 2023 May 1.
Water footprint (WF) assessments have become a significant tool for the sustainable management in recent years. Effective rainfall (P) is a critical indicator for characterizing soil moisture (green water, WF) and calculating irrigation requirements (blue water, WF). However, majority of the water footprint analyses employ empirical or numerical models to predict P, and the number of studies for experimental validation of these models are quite insufficient. The main scope of this study is to test the performance of commonly used P estimation models in relation to the soil water balance (SWB) of an experimental site. Accordingly, the daily and monthly soil water budget is estimated from a maize field which is characterized as semi-arid land with continental climate (Ankara, Turkey), equipped with moisture sensors. Then, P, WF, and WF parameters are calculated using FP, US-BR, USDA-SCS, FAO/AGLW, CROPWAT, and SuET methods and compared with SWB method. Employed models were highly variable. CROPWAT and US-BR predictions were the most accurate. In majority of months, the CROPWAT method estimated the P with a maximum deviation of 5% from the SWB method. In addition, the CROPWAT method predicted blue WF with an error less than 1%. The widely utilized USDA-SCS approach did not produce expected results. The FAO-AGLW method provided the lowest performance for each parameter. We also find that the errors in estimating P in semi-arid conditions cause green and blue WF outputs to be quite less accurate than the dry and humid cases. This study provides one of the most detailed assessments about the impact of effective rainfall on the blue and green WF results with high temporal resolution. The findings of this study are important for the accuracy and performance of the formulae used in P estimations and to develop more precise blue and green WF analyses in the future.
近年来，水足迹（WF）评估已成为可持续管理的一项重要工具。有效降雨量（P）是表征土壤水分（绿水，WF）和计算灌溉需求（蓝水，WF）的关键指标。然而，大多数水足迹分析采用经验或数值模型来预测P，而对这些模型进行实验验证的研究数量相当不足。本研究的主要范围是测试常用P估算模型与实验场地土壤水平衡（SWB）相关的性能。因此，通过一个配备了湿度传感器、具有大陆性气候的半干旱土地（土耳其安卡拉）的玉米田来估算每日和每月的土壤水分收支。然后，使用FP、US-BR、USDA-SCS、FAO/AGLW、CROPWAT和SuET方法计算P、WF和WF参数，并与SWB方法进行比较。所采用的模型差异很大。CROPWAT和US-BR的预测最为准确。在大多数月份，CROPWAT方法估算的P与SWB方法的最大偏差为5%。此外，CROPWAT方法预测蓝水足迹的误差小于1%。广泛使用的USDA-SCS方法未产生预期结果。FAO-AGLW方法对每个参数的性能最低。我们还发现，在半干旱条件下估算P时的误差会导致绿水和蓝水足迹的输出比干旱和湿润情况的准确性低得多。本研究提供了关于有效降雨对蓝水和绿水足迹结果影响的最详细评估之一，具有高时间分辨率。本研究的结果对于P估算中使用的公式的准确性和性能以及未来开发更精确的蓝水和绿水足迹分析非常重要。
